The $100K Deployment Mistake: How One Line of Code Changed Everything
A legacy code issue nearly derailed our Black Friday sales, but targeted interventions saved the day. Here’s how we turned chaos into clarity.
A single line of legacy code almost cost us our Black Friday sales—don't let that happen to you.Back to all posts
Your payment system just crashed on Black Friday, and the culprit? A single line of legacy code that no one even thought to check. As customers flooded in to snag their holiday deals, our team was left scrambling. The result? $100K in refunds for failed transactions and a hit to our brand reputation that would take far
longer to recover from. This was the moment we realized that our approach to deployment was fundamentally flawed, and it was time to rethink our strategy.
The stakes couldn’t be higher. For engineering leaders, this isn’t just about code—it's about trust. Customers expect seamless experiences, especially during peak times. When we analyzed our metrics, it became clear that our Mean Time to Recovery (MTTR) was sitting at an embarrassing 12 hours, with a change failure率 of
over 30%. This wasn’t sustainable, and we needed a game plan fast. We had to adopt a more robust deployment strategy that could withstand the pressures of high traffic while minimizing risk.
We started by implementing a series of interventions focused on both our tooling and processes. First, we integrated an observability stack using tools like Grafana and Prometheus. This allowed us to visualize our system's health in real time, making it easier to identify and address issues before they escalated. Next,
we adopted GitOps practices, enabling us to manage our deployments through version control. This not only improved our velocity but also reduced our change failure rate to below 10%. We also instituted a culture of blameless postmortems, which encouraged team members to learn from mistakes without fear of retribution.
The outcomes were significant. Within three months, our MTTR dropped from 12 hours to just 2 hours, and our change failure rate decreased to 5%. We transitioned from a chaotic deployment process to a streamlined, predictable cadence. Our customers noticed the difference, and our reputation began to recover. In the end,
Key takeaways
- Legacy code can bring down your entire system unexpectedly.
- Investing in observability tools can drastically improve MTTR.
- Frequent small deployments reduce change failure rates.
Implementation checklist
- Implement automated monitoring for legacy systems using Prometheus.
- Conduct weekly retrospectives focused on deployment failures.
- Adopt a CI/CD approach with GitOps practices for better visibility.
Questions we hear from teams
- What tools should we consider for observability?
- Grafana and Prometheus are excellent choices for real-time monitoring and visualization.
- How can we reduce our change failure rate?
- Adopt smaller, more frequent deployments and implement automated tests to catch issues earlier.
- What’s the best way to handle legacy code?
- Regularly review and refactor legacy code, and consider using feature flags to manage changes safely.
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